Dense Retrieval with Continuous Explicit Feedback for Systematic Review Screening Prioritisation

X Mao, S Zhuang, B Koopman, G Zuccon - Proceedings of the 47th …, 2024 - dl.acm.org
The goal of screening prioritisation in systematic reviews is to identify relevant documents
with high recall and rank them in early positions for review. This saves reviewing effort if …

Query understanding in the age of large language models

A Anand, A Anand, V Setty - arXiv preprint arXiv:2306.16004, 2023 - arxiv.org
Querying, conversing, and controlling search and information-seeking interfaces using
natural language are fast becoming ubiquitous with the rise and adoption of large-language …

Measuring Retrieval Complexity in Question Answering Systems

M Gabburo, NP Jedema, S Garg, LFR Ribeiro… - arXiv preprint arXiv …, 2024 - arxiv.org
In this paper, we investigate which questions are challenging for retrieval-based Question
Answering (QA). We (i) propose retrieval complexity (RC), a novel metric conditioned on the …

Optimizing Test-Time Query Representations for Dense Retrieval

M Sung, J Park, J Kang, D Chen, J Lee - arXiv preprint arXiv:2205.12680, 2022 - arxiv.org
Recent developments of dense retrieval rely on quality representations of queries and
contexts from pre-trained query and context encoders. In this paper, we introduce TOUR …

UKP-SQuARE v3: A Platform for Multi-Agent QA Research

H Puerto, T Baumgärtner, R Sachdeva, H Fang… - arXiv preprint arXiv …, 2023 - arxiv.org
The continuous development of Question Answering (QA) datasets has drawn the research
community's attention toward multi-domain models. A popular approach is to use multi …

Leveraging FAQ systems: integrating explainable AI and user feedback into a fine-tuned BERT model/Author Samawah Hassan

S Hassan - 2023 - epub.jku.at
An FAQ retrieval system is a question-answering system designed to support users by
retrieving a ranked list of QA pairs relevant to their queries. This research aims to improve …